We present an algorithm for the segmentation of multicell fluorescence microscopy images. Such images abound and a segmentation algorithm robust to different experimental conditions as well as cell types is becoming a necessity. In cellular imaging, among the most often used segmentation algorithms is seeded watershed. One of its features is that it tends to oversegment, splitting the cells, as well as create segmented regions much larger than a true cell. This can be an advantage (the entire cell is within the region) as well as a disadvantage (a large amount of background noise is included). We present an algorithm which segments with tight contours by building upon an active contour algorithm-STACS, by Pluempitiwiriyawej et al. We adapt the algorithm to suit the needs of our data and use another technique, topology preservation by Han et al., to build our topology preserving STACS (TPSTACS). Our algorithm significantly outperforms the seeded watershed both visually as well as by standard measures of segmentation quality: recall/precision, area similarity and area overlap.
SEGMENTATION OF FLUORESCENCE MICROSCOPY IMAGESFluorescence microscopy is one of the main ways for biologists to observe processes in a live cell. As collection of fluorescence microscopy data sets continues, automated and robust processing methods are becoming increasingly important. One common task in such systems is segmentation when acquired images contain more than one cell. This is a basic (and very hard) problem in image processing. It aims to separate an object of interest from other objects and the background. Its result is a closed curve around the object of interest called a contour.An example of automated processing mentioned above is the system for classification of proteins based on fluorescence microscopy images of their subcellular locations (spatial distributions within the cell) [1]. The data set contained parallel images for a specific protein, total protein and total DNA. Segmentation was performed using the seeded watershed algorithm on the total protein channel using the nuclei as seeds [2], and was modified to exclude partial cells on the boundaries [1]. In this paper, we use the same data set and compare the results of our algorithm to those obtained by seeded watershed (SW) in [1].
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